Understanding the effects of treatment with potent anti-viral drugs is critical for epidemic control. For example, the drugs have the unwanted effect of inducing resistant strains of HIV that can cause return of high viral burden within patients as well as dissemination of drug resistance throughout populations at risk. This application continues to address the statistical problems that arise in studying predictors of virological rebound, and the nature of the virus that rebounds. We propose statistical methods for identifying different genotypic classes in order to evaluate: how viral sequences evolve in response to treatment and whether classes of sequences exist across which either the order of accumulation of mutations or the rates at which they occur differ; how treatment choices affect which mutations develop and the rates at which they occur; the extent to which these classes are predictive of drug susceptibility phenotype or RNA response; and, whether knowing the history of a viral population provides predictive information about patient response to a new therapy. These are high-dimensional problems and to solve them we propose novel approaches based on distance methods. We also focus on modeling the HIV RNA viral trajectory over time with emphasis on the stochastic nature of the viral trajectories after initiation of anti-retroviral treatment. The overall goal is to develop methods for assessing the effects of treatments, and other coyariates on viral trajectories, and to predict viral trajectories in individuals. This is challenging because the patterns of viral trajectory vary greatly across subjects, measurements are often censored due to assay limitations, and data are often missing.